📘 Research Topics on Large-Scale Architectural Heritage Reconstruction
High-fidelity textured 3D models have become essential for documenting structural damage, guiding restoration decisions, and enhancing visual communication in architectural heritage conservation. Traditional LiDAR-based texture modeling is often impractical for large-scale sites due to their vastness, terrain complexity, and gaps in structural capture. Recent research explores alternative reconstruction approaches that overcome these limitations while maintaining high geometric accuracy and visual quality.
Limitations of LiDAR-Based Texture Modeling in Large-Scale Heritage Sites
Large architectural heritage sites often suffer from incomplete LiDAR scans caused by occlusions, irregular topography, and scale constraints. These limitations reduce the reliability of LiDAR-derived textures and restrict their role in conservation workflows. Understanding these challenges highlights the need for alternative acquisition and modeling techniques that can capture complex geometries more efficiently across large spatial extents.
Multi-View Stereo Networks for Dense Point Cloud Generation
The adoption of multi-view stereo (MVS) networks offers a promising solution by generating dense point clouds directly from aerial images. By leveraging learned pixel correspondence and depth estimation, MVS models produce rich geometric detail without the logistical barriers of LiDAR. This enables scalable and cost-effective 3D reconstruction suitable for expansive heritage landscapes.
Textured Mesh Construction for Architectural Heritage Applications
Once dense point clouds are produced, surface reconstruction techniques are applied to create high-quality textured meshes. These textured models help conservators visually interpret deterioration patterns, structural anomalies, and spatial relationships. The resulting meshes support both diagnostic analysis and immersive documentation, strengthening their role in digital preservation workflows.
Benchmark Evaluation Using DTU and Tanks and Temples Datasets
The proposed reconstruction pipeline demonstrates competitive performance on widely recognized benchmark datasets such as DTU and Tanks and Temples. These evaluations confirm the method's accuracy, completeness, and robustness across controlled and real-world imaging conditions. Such validations ensure the pipeline meets research and professional standards for heritage 3D modeling.
Real-World Validation on the Great Wall’s Pan Longshan Section
Applying the pipeline to the Pan Longshan section of the Great Wall illustrates its practical effectiveness. Comparative experiments with commercial tools like Meta shape and Pix4D reveal improved texture fidelity, smoother surfaces, and more coherent structural reconstruction. This real-world validation highlights the adaptability and potential of the proposed approach for large-scale heritage conservation projects.
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